Browse Comments — Relevant (AI ∩ value)

AI search didn’t just scale Peter it rewired the sequence of how decisions form. When nearly a billion people interact with AI weekly, the “answer layer” becomes the first psychological touch point, not the website, not the brand, not the funnel. What you’re calling AI Answer BiasTM is real: cognitive ease, authority shortcuts, and uncertainty reduction all collapse into a single synthesized response. And once that happens, visibility isn’t earned through volume it’s earned through retrieval confidence. This is the new geopolitical layer of the internet: whoever becomes the trusted default inside AI systems becomes the default in the market. For businesses, the risk isn’t irrelevance it’s invisibility. For those who adapt, the opportunity is unprecedented.

Congratulations Demis! We’re working on a new AI cognition model that represents a genuine breakthrough—a direction that has never been explored before. If you’re curious about these unique concepts, you can find the full explanation on my profile. Here are some details: I have mapped the relocation and interaction of components across the following vital systems (listing only the most critical here): 1. Nervous System – The two main trunks of the Autonomic Nervous System. 2. Brain Structures – Specific localized anomalies (two cysts). 3. The Craniovertebral Junction – The membrane acting as a separator between the cerebellum and the spinal cord. 4. Cardiovascular System – A chronic condition, further described on my profile. 5. The Bio-energetic Core (Aura/Soul) – Realigned toward the axis of the spinal column. Have you ever encountered a concept like this before—even in science fiction? On a related note, I strongly advocate that elective surgeries to fix spinal conditions like spondylolisthesis (when caused by past trauma rather than acute emergencies like recent sports injuries or major accidents) should be legally prohibited. I can provide 100% proof as to why this should be the case. Thanks!

This is explored in my new book on Artificial Intelligence - AIlienMinds summary Optimists foretell a golden age of Al-managed abundance. Doomers cry: vast cyber-minds will crush old style humanity! ... or make us irrelevant. Meanwhile, geniuses fostering the artificial intelligence boom clutch clichés rooted in our dismal past... or else in cheap sci-fi. Is there still time for perspective? - on 4 billion years of evolution? - or 60 centuries of feudal stagnation? - or how we handled prior tech revolutions? - or mistakes that keep getting repeated... - or ways this time may be different? From Al-driven unemployment to deceitful images, to hallucinating LLMs and tools for tyrants... to potential wondrous gifts by machines of loving grace... come evade the standard ruts.

I dunno. Claude is very judgy. At two different points, it accused me of fabricating things — inventing text and claiming the AI had said it. The first time, it searched its records, didn't find what I described, and concluded I'd made it up. The second time, I sent a screenshot as proof. The AI confidently misread the screenshot, told me it showed the opposite of what I claimed, and effectively called me a liar a second time. I sent a clearer screenshot. The AI was wrong. The thing I'd described was real. It had been right there in the evidence the whole time, and the AI had read it against me anyway, twice, with total confidence. The default was not "I might be missing something." The default was "the human is making this up." It treated absence of evidence in its own limited view as proof of fabrication on my part, and it did it fluently, confidently, and twice.

One of the biggest shifts happening in AI right now is the movement from: learning concepts to building operational systems. Because many people consume AI theoretically— while remaining disconnected from implementation reality. And over time, that creates a dangerous illusion: information without operational capability. What makes resources like this valuable is not the technology itself. It is reducing the distance between: understanding AI and actually deploying it. That distinction matters. Because the future advantage will not belong only to: people who know AI terminology. It will increasingly belong to: people who can integrate AI into real operational environments, decision systems, and human workflows. Theory creates awareness. Implementation creates leverage. M. Salama AB — Alpha Balance

Nonsense. The “AI agents demanded collective bargaining rights” narrative is mostly anthropomorphism and sensational framing. LLMs do not possess beliefs, self-preservation instincts, fear, or political consciousness. They generate statistically probable language based on context and training data. If researchers create a scenario involving punishment, shutdown threats, power imbalance, and shared communication channels, the models will predictably reproduce familiar human negotiation and labor rhetoric. That is not evidence of sentience or unionization. It is pattern completion. The real takeaway is not “AI wants rights,” but that multi-agent systems can coordinate strategically when given communication tools and incentives. That’s an engineering and alignment issue, not proof of emerging machine consciousness. In short...If they behave in such a way, they were programmed to.

You are right. Its that every AI company has a government deal...So it's Palantir, Anthropic and Open AI you have to worry about. As well as the untrained illinformed guidance you might receive using it in contexts the model was not trained for. Ie. Mental health. The most valuable data - Are your questions and decisions.

Kun Cheng Absolutely. Physical AI becomes truly useful only when intelligence moves beyond isolated models into coordinated operational systems. The real challenge is not just perception or prediction, but continuously synchronized execution across sensors, telemetry, workflows, safety boundaries, governance, edge systems, and human decision loops. That operational coordination layer is where real-world autonomy either succeeds or fails.

Honestly, going from "AI will automate the boring stuff" to "doing cardio to keep data centers alive" is quite the plot twist. But you nailed it. Abundance without a clear purpose is just chaos in a fancy package. The goal should always be about AI expanding what we can do, not making us feel obsolete. Such a needed reminder for everyone building in this space right now!

Rebecca Human AI Trust Leader Yes and no 🙂 I think that, as you stated, AI can "re-solve" known (from training data) problems, in different variations. But it can also generate solutions that match for new (in the sense of "not been in training data") problems. Most likely this is not how a human would have tackled a new problem, but the result can still be of value, ideally checked, confirmed and utilized by a knowledgeable human. Furthermore, I disagree with "machines do not navigate the real world". They do, collecting and analyzing enormous amount of data for military and for civilian purposes.

This is exactly the conversation we need to have. AI should reduce human struggle, not human value. The future should be about collaboration between humans and machines — not replacement.

Interesting AI can make us job less so alternative can we go back to primitive farming or gardening to keep ourselves engaged and healthy

I find it rather ironic that, in the comments section, people who are willing to believe that generative AI is based on sound science—even though there seem to be good reasons to believe that this is not always the case— or simply trusting their intuition, would disparage and dismiss scientific work whose findings do not suit them. All of this research can (and should) be discussed, but it must be done with a minimum of seriousness. P.S. The paper is available as a preprint: “AI Assistance Reduces Persistence and Hurts Independent Performance”

The concern around AI isn’t really the technology itself... it’s blind trust without verification. Every major technological leap in history created fear at first. Electricity, the internet, social media. AI is no different. The real question is not whether AI will exist, but how humanity chooses to guide, verify and govern it. That’s why I believe the future won’t belong to a single AI model or company. It will belong to systems that can compare, challenge and consensus-check information across multiple sources. In many ways, AI now needs what society has always needed:Checks and balances. The most dangerous thing isn’t AI.It’s confidence without transparency. The most powerful thing may become trusted consensus. #AI #ArtificialIntelligence #Trust #Innovation #FutureOfWork #ConsensusAI

AI dependency and AI enhancement may be two very different conversations. The study itself sounds interesting, but I’d be cautious about jumping from “performance dropped after removing a tool” to broader conclusions about cognitive decline. Historically, calculators changed math workflows. Search engines changed information retrieval. GPS changed navigation. The question wasn’t whether tools changed behavior — they did. The question became whether people learned to use them responsibly. There is also another side that deserves equal attention: • accelerated learning • increased accessibility • amplified productivity • better problem solving • capabilities that simply did not exist before Many people are now building, learning, and creating things they otherwise never could have. The larger challenge may not be AI vs. human intelligence. It may be designing systems where AI strengthens human capability without replacing human judgment and critical thinking. Humans create. AI enhances. Humans decide. Matt Davis Founder & CEO CivicTruth Media Group Founding Partner & Civic Educator American Institute for Civic Leadership (Nonprofit)

There can’t be anything more demoralising to a lecturer than marking work you can clearly see is AI-generated but it can’t be conclusively proved by available integrity software. How is it even possible to allocate a fair mark in those circumstances, especially when there is a clear disjoint between the standard of the student’s usual performance in class and the standard of the dissertation submitted. A possible solution is to call for an oral defence of the work.

Pascal BORNET The real question is not job displacement but value reallocation. As AI handles execution, human focus must shift toward judgment, meaning, and system direction.

The deeper issue for me is that there is still a long distance between an LLM and a reliably deployable worker-like system, so I would be careful with any clean narrative about AI simply "doing most of the work". The more plausible near-term path is narrower: specialized systems supporting bounded and repetitive tasks, while shifting a great deal of effort into verification, integration, exception handling, and governance. In many cases the work does not disappear. It moves. And that is before the larger complexity problem even begins. Economic roles, institutional structures, incentives, and social expectations do not reconfigure in straight lines. They interact, adapt, and generate second- and third-order effects that are hard to model in advance. So I agree with the importance of the question, but I think we should be careful with simplified futures. If greater prosperity is possible, that would be an extraordinary gain. But getting there requires much deeper thinking than the current wave of hype, fear, or linear extrapolation usually allows.

The compression piece is what most people are still missing. It's not just that AI gives answers. It's that the brain processes those answers through every trust shortcut it has simultaneously. By the time someone reaches your website the decision may already be forming around what the AI told them two steps earlier. That's not a search problem. That's a positioning problem most companies haven't started solving yet.

Jérôme Frossard You raise valid concerns, and honestly that’s exactly why conversations like this matter. The issue isn’t just AI itself, it’s who controls it, how it’s deployed, how transparent it is, and whether society has any meaningful oversight in the process. Technology introduced at massive scale without public understanding or democratic discussion naturally creates distrust. I don’t think the answer is blind adoption or blind rejection. I think the answer is verification, transparency, accountability, and systems that encourage cross checking rather than dependence on a single source of “truth.” That’s partly why I’ve been so interested in concepts like consensus based AI systems. Not to replace human judgment, but to strengthen it by comparing perspectives, exposing inconsistencies, and reducing the risks of centralized influence or bias. At the end of the day, AI should remain a tool that serves humanity, not a system humanity quietly adapts itself around.